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Verification of GPU Program Optimizations in Lean

Björn Fischer

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Tags: Computer science, High-level Languages, Package, Thesis

October 6, 2019 by hgpu

waLBerla: A block-structured high-performance framework for multiphysics simulations

Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde

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Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100

October 6, 2019 by hgpu

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Maria Papadaki, Yannis Sfakianakis, Christos Kozanitis, Angelos Bilas

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Tags: Computer science, CUDA, GPU cluster, Grid, nVidia, nVidia Quadro K 2200

October 6, 2019 by hgpu

MIOpen: An Open Source Library For Deep Learning Primitives

Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package

October 6, 2019 by hgpu

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Tesla V100

September 29, 2019 by hgpu

Futhark Vulkan Backend

Steffen Holst Larsen

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Tags: Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, Vulkan

September 29, 2019 by hgpu

Heterogeneous Resource-Elastic Management for FPGAs: Concepts, Theory and Implementation

Anuj Vaishnav, Dirk Koch, Khoa Dang Pham

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, OpenCL

September 29, 2019 by hgpu

Elastic deep learning in multi-tenant GPU cluster

Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, James Cheng

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Tags: Computer science, Deep learning, Distributed computing, GPU cluster, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

September 29, 2019 by hgpu

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut

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Tags: Artificial intelligence, Computer science, Deep learning, NLP, TPU

September 29, 2019 by hgpu

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia DGX-2, Package, Tesla V100

September 22, 2019 by hgpu

Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu

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Tags: AI, AMD Radeon R7, ATI, Computer science, Deep learning, Neural networks, nVidia, PyTorch, TensorFlow, Tesla P100, Tesla V100, TPU

September 22, 2019 by hgpu

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Abhinav Jangda, Arjun Guha

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Tags: CUDA, Image procesing, nVidia, nVidia GeForce GTX 1080 Ti, Python, Tesla V100

September 22, 2019 by hgpu

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

HPX: a C++ Standard Library for Concurrency and Parallelism

Asynchronous-Many-Task Systems: Challenges and Opportunities – Scaling an AMR Astrophysics Code on Exascale machines using Kokkos and HPX

TorchQC: Quantum Dynamics and Machine Learning

TorchQC – A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control

Matrix multiplication using Tensor Cores in CUDA

Utilizing Tensor Cores in Futhark

CPP Joules: Energy Measurement tool for CPP/CUDA programs

CPPJoules: An Energy Measurement Tool for C++

HPC-Coder-v2

HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languages

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

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